Migration Guide¶

This page contains information about changes between major versions and how you can migrate from one version to another.

0.12.x to 0.13.0¶

Warning

This is a release breaking backwards compatibility. Unfortunately, it is not possible to load previously trained models as the parameters for the tensorflow and CRF models changed.

0.11.x to 0.12.0¶

Warning

This is a release breaking backwards compatibility. Unfortunately, it is not possible to load previously trained models (as the stored file formats have changed as well as the configuration and metadata). Please make sure to retrain a model before trying to use it with this improved version.

model configuration¶

We have split the configuration in a model configuration and parameters used to configure the server, train, and evaluate scripts. The configuration file now only contains the pipeline as well as the language parameters. Example:

langauge: "en"

pipeline:
- name: "nlp_spacy"
  model: "en"               # parameter of the spacy component
- name: "ner_synonyms"

All other parameters have either been moved to the scripts for training (train_parameters), serving models (Server Parameters), or put into the pipeline configuration (Pipeline and Component Configuration).

persistors:¶

renamed AWS_REGION to AWS_DEFAULT_REGION
always make sure to specify the bucket using env BUCKET_NAME
are now configured solely over environment variables

0.9.x to 0.10.0¶

We introduced a new concept called a project. You can have multiple versions of a model trained for a project. E.g. you can train an initial model and add more training data and retrain that project. This will result in a new model version for the same project. This allows you to, allways request the latest model version from the http server and makes the model handling more structured.
If you want to reuse trained models you need to move them in a directory named after the project. E.g. if you already got a trained model in directory my_root/model_20170628-002704 you need to move that to my_root/my_project/model_20170628-002704. Your new projects name will be my_project and you can query the model using the http server using curl http://localhost:5000/parse?q=hello%20there&project=my_project
Docs moved to https://rasahq.github.io/rasa_nlu/
Renamed name parameter to project. This means for training requests you now need to pass the project parameter instead of ``name, e.g. POST /train?project=my_project_name with the body of the request containing the training data
Adapted remote cloud storages to support projects. This is a backwards incompatible change, and unfortunately you need to retrain uploaded models and reupload them.

0.8.x to 0.9.x¶

add tokenizer_spacy to trained spacy_sklearn models metadata (right after the nlp_spacy). alternative is to retrain the model

0.7.x to 0.8.x¶

The training and loading capability for the spacy entity extraction was dropped in favor of the new CRF extractor. That means models need to be retrained using the crf extractor.
The parameter and configuration value name of backend changed to pipeline.
There have been changes to the model metadata format. You can either retrain your models or change the stored metadata.json:
- rename language_name to language
- rename backend to pipeline
- for mitie models you need to replace feature_extractor with mitie_feature_extractor_fingerprint. That fingerprint depends on the language you are using, for en it is "mitie_feature_extractor_fingerprint": 10023965992282753551.

0.6.x to 0.7.x¶

The parameter and configuration value name of server_model_dir changed to server_model_dirs.
The parameter and configuration value name of write changed to response_log. It now configures the directory where the logs should be written to (not a file!)

The model metadata format has changed. All paths are now relative with respect to the path specified in the configuration during training and loading. If you want to run models that are trained with a version prev to 0.7 you need to adapt the paths manually in metadata.json from

{
    "trained_at": "20170304-191111",
    "intent_classifier": "model_XXXX_YYYY_ZZZZ/intent_classifier.pkl",
    "training_data": "model_XXXX_YYYY_ZZZZ/training_data.json",
    "language_name": "en",
    "entity_extractor": "model_XXXX_YYYY_ZZZZ/ner",
    "feature_extractor": null,
    "backend": "spacy_sklearn"
}

to something along the lines of this (making all paths relative to the models base dir, which is model_XXXX_YYYY_ZZZZ/):

{
    "trained_at": "20170304-191111",
    "intent_classifier": "intent_classifier.pkl",
    "training_data": "training_data.json",
    "language_name": "en",
    "entity_synonyms": null,
    "entity_extractor": "ner",
    "feature_extractor": null,
    "backend": "spacy_sklearn"
}